Method and device for adjusting a regulating device

ABSTRACT

The aim of the invention is to provide a regulating device which ensures automatic monitoring of additional regulator parameters, and a method for said automatic monitoring. To this end, the method and the device for adjusting a first ( 2 ) and a second functional unit ( 1, 3, 4, 6 ) of a regulating device are characterised in that, in a first step, a code is determined, which quantifies the relative variance between the reactions y Mod  and y Sys  of both functional units in relation to a control variable variation during the stimulation of both functional units using the same control variable variation, and in a second step, a corrective factor is determined from the code and used to parameterise at least one of the two functional units ( 1, 2, 3, 4, 6 ), reducing the relative variance. The invention is advantageous in that the regulating circuit can be automatically adapted to new conditions at any time.

The invention involves a method for adjusting a first and a second functional unit of a regulating device and also involves the regulating device itself, on which the execution of the method is based.

Controlled systems with an integrated branch in the controller have structurally dictated overshoots in the guidance behavior. Such controlled systems are used, for example, in speed-controlled AC servomotors or also in pressure-controlled cylinder drive units. In order to reduce these overshoots, setpoint filters, for example, are used, which, depending on how they are parameterized, are able to influence the overshoots.

Designs of this kind, however have the disadvantage that with the filter delay, there is an additional controller parameter that must be tracked during operation since the peripheral circumstances (controlled system parameters) can change at any time.

The object of the present invention is to disclose a regulating device, which can ensure an automatic tracking of additional control parameters, and a method by means of which an automatic tracking can be carried out.

The object is attained by means of a method and a regulating device as recited in the independent claims. The regulating device carries out an adjustment for at least two functional units included in the regulating device, which functional units can be configured by means of parameterizable eigenvalues; the regulating device includes a calculation means that determines a characteristic number, which quantifies the relative deviation between the reactions y_(Mod) and y_(Sys) of the two functional units to a reference variable change that occurs when both functional units are excited by the same reference variable change and which, based on the characteristic number, determines a correction factor that is used for parameterizing the functional units; a correction means is included to which the correction factor is supplied and which carries out a parameterization of the functional units in such a way that this reduces the relative deviation.

The method according to which the regulating device functions here is structured so that the characteristic number is determined in a first step. The characteristic number is determined based on the different responses of the two functional units to the occurrence of a reference variable change and quantifies a possible parameter deviation between the two functional units. The correction factor is determined in a second step based on this characteristic number; the correction factor is used to parameterize at least one of the two functional units and to reduce the relative deviation. The correction factor can relate to the filter time and/or to a controller amplification K_(P). It produces a convergence of desired model behavior and system behavior. With a suitable parameterization of the functional units of the regulating device according to the invention, it is thus possible to reduce overshoots and to partially decouple the interference behavior of the system from guidance behavior.

The calculation means of the regulating device is embodied so that it is possible to determine a nondimensional correction factor and through the use of the correction means, to carry out an iterative change to an eigenvalue of at least one functional unit by means of the correction factor; the correction means continues to process correction values from the calculation means until the deviation essentially trends toward zero or, in accordance with a setpoint value, essentially corresponds to this setpoint value.

The advantage of this embodiment lies in the fact that the regulating device includes a self-regulating mechanism that is able to dynamically and adaptively carry out an optimization in that by means of the above-mentioned parameterization of the functional units, the control loop can be automatically adapted to new circumstances at any time. This mechanism could also be started in a non-automatic fashion, i.e. manually, for example in the mobile hydraulics in speed-controlled axes in construction machines, where either an automated control level is not present or automated axis movements for adjustment purposes are not admissible.

Preferably as part of a third process step, the adjustment is started by means of a signal derived from the reference variable change so that the adjustment can be easily automated. This could be implemented in such a way that the regulating device on which the invention is based includes a triggering means, which, based on a reference variable change, derives a signal that is supplied to the regulating device and triggers the start of the adjustment.

In a concrete embodiment, the first functional unit can be a setpoint filter implemented in the form of a low-pass filter in the integral branch of the regulating device and the second functional unit can be the system itself, which is to be regulated and is composed of at least one controller, a controlled system, and customary interference variables.

Because the signal at the filter output and the controlled variable of the real system are identical, if interference phenomena in the real system are infinitesimal, then the filter more or less represents an adjustable model of the system, with the adjustment being carried out by means of the method according to the invention. It is thus possible for controlled system variables such as inertias or hydraulic capacities to be derived from the behavior of the controlled system, in an automated or partially automated fashion under certain boundary conditions.

Preferably, each functional unit is preceded by an additional filter, with the respective filters having an identical filter characteristic and thus permitting the concerted reduction of interference influences. It is thus possible to filter y_(Mod) and Y_(Sys) before the characteristic number determination, thus reducing interference phenomena.

The error detection is based on the comparison values y_(Mod) and y_(Sys), where y_(Mod) and Y_(Sys) are the transient responses of the two functional units (filter/system in this case) when a change in the reference variable occurs over time. An error signal E=y_(Mod)−y_(Sys) over a reference period T_(f) is added up by means of integration. T_(f) corresponds to an eigenvalue (sometimes the time constant in a PT1-element) by means of which it is possible to influence the transient response of at least one of the two functional units.

For the problem at hand, a weighting approach was selected, which is chiefly aimed at the robustness of the algorithm in the presence of interference influences. This involves the following considerations: interference phenomena act on only the real system and thus influence y_(Sys). Interference influences, however, do not influence the model and thus the y_(Mod). It is more certain that an error signal E is a result of the guidance signal, the greater the excitation of the system by means of the guidance signal because in that case, the amplitude of the interference variables is negligible.

In a system of the first order (PT1), the change of the output signal is a measure for the excitation since it reflects the deviation between the input and output signal in accordance with:

${\overset{.}{y}}_{Mod} = {\frac{1}{T_{f}} \cdot {\left( {x - y_{Mod}} \right).}}$

The error signal E is thus weighted with the value {dot over (y)}_(Mod) because {dot over (y)}_(Mod) changes proportionally to the intensity of the reference variable. The weighting of the error signal E through multiplication by the amount {dot over (y)}_(Mod) ensures that errors are not weighted as long as no reference variable changes occur.

There is thus initially an integrated weighted error (IWE) available for the error evaluation, in accordance with:

$\begin{matrix} {{IWE} = {\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}\mspace{14mu} {or}}}} & (1) \\ {{IWE} = {\int_{0}^{T_{{Re}\; f}}{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}} & (2) \end{matrix}$

each of which is a special error functional for determining dynamic parameter values of a control system on the basis of time series after excitation of the system. The difference between the two functionals lies in the fact that in functional (1), IWE is determined taking into account a freely selectable starting time T0, whereas in functional (2), IWE is determined taking into account the starting time T0=0. Functional (2) thus represents a special case of functional (1).

The disadvantage of functional (1) or (2) with regard to determining parameter value ratios is that they have a dimension y² and are thus disproportionately dependent on the level of the excitation. A nondimensional, excitation-independent parameter value can be achieved if the IWE functionals are related to a functional that is determined in an entirely similar fashion, but through the use of the system output parameters instead of the error E and through the use of the change speed of the system output instead of the model output, in accordance with

$\begin{matrix} {\int_{\;_{0}}^{T_{{Re}\; f}}{{{\ {{\overset{.}{y}}_{Sys} \cdot y_{Sys}}} \cdot {{sgn}\left( y_{Sys} \right)} \cdot {t}}\mspace{14mu} {or}}} & (3) \\ {\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{{\ {{{\overset{.}{y}}_{Sys} \cdot \Delta}\; y_{Sys}}} \cdot {{sgn}\left( {\Delta \; y_{Sys}} \right)} \cdot {t}}\mspace{14mu} {or}}} & (4) \\ {\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}\ {{{\overset{.}{y}}_{Sys} \cdot \Delta}\; {y_{Sys} \cdot {{t}.}}}} & (5) \end{matrix}$

The signum function in scenarios (3) and (4) ensures that the direction of the error is independent of the direction of the excitation. Using the system response Y_(Sys) to normalize the error functional also achieves a further stabilization in relation to interference phenomena. Using Δy_(Sys)=y_(Sys)−y_(Sys)(t=T₀) in scenario (5) takes into account the cumulative state change of the system and using it in scenario (4) takes into account the total state change of the system. The normalization achieves the decoupling of the result from the form of excitation and level of excitation. The normalization with regard to the total state change gives less weight to the error in noisy signals, i.e. yields a good interference stability. The normalization with regard to the cumulative state change converges more quickly (discussed later).

On the whole, this yields the following alternative functionals for determining the characteristic number by means of which a characteristic number for corrections can be derived:

$\begin{matrix} {{{characteristic}\mspace{14mu} {number}} = {\frac{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\ {{{\overset{.}{y}}_{Sys} \cdot \Delta}\; y_{Sys}}} \cdot {{sgn}\left( {\Delta \; y_{Sys}} \right)} \cdot {t}}}\mspace{14mu} {or}}} & (6) \\ \begin{matrix} {{{characteristic}\mspace{14mu} {number}} = {\frac{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\overset{.}{y}}_{Mod} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{{\overset{.}{y}}_{Sys} \cdot \Delta}\; {y_{Sys} \cdot {t}}}}\mspace{14mu} {or}}} & \; \end{matrix} & (7) \\ {{{{characteristic}\mspace{14mu} {number}} = \frac{\int_{0}^{x \cdot T_{f}}{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{0}^{x \cdot T_{f}}{{\ {{\overset{.}{y}}_{Sys} \cdot \; y_{Sys}}} \cdot {{sgn}\left( y_{Sys} \right)} \cdot {t}}}},} & (8) \end{matrix}$

where y_(Mod) and y_(Sys) represent the transient responses of the two functional units to a reference variable change, taken into account over time. The symbol {dot over (y)} is the time derivative of y and T_(f) is an eigenvalue, in particular the filter time constant of a low-pass filter, by means of which the transient response of at least one of the two functional units can be influenced. The letter x stands for a multiple of the eigenvalue, preferably for a value in the vicinity of 2*Pi, and the equation Δy_(Sys)=y_(Sys)−y_(Sys)(t=T₀) applies, i.e. the system change between two points in time is now taken into account, where T0 is a freely selectable starting time in relation to which the current time point is considered.

This characteristic number is referred to as a relative integrated weighted error (RIWE).

To simplify the adjustment, a characteristic curve for the characteristic number is determined as a function of the ratio of the eigenvalues of the first and second functional unit, which characteristic curve is in particular at least partially linearized in the region in which the characteristic number trends toward zero. For this reason, the regulating device includes a means for deriving a characteristic curve for the characteristic number as a function of the eigenvalues of the functional units and preferably includes a storage means in which this characteristic curve is stored and to which the regulating device has access.

FIG. 2 shows an example of such a characteristic curve.

The use of the RIWE functional in this case was based on two ideal systems of the first order. This yields a characteristic curve of the error functional as a function of the ratio of the time parameters T_(Sys)/T_(Mod) of the compared systems.

The determination of the correction factor by means of the characteristic curve is then preferably carried out through successive approximation within a definite value range of the characteristic number and limitation of the characteristic number outside of this value range.

A rule for the correction estimate can, for example, be expressed as follows:

$\left( \frac{T_{Mod}}{T_{Sys}} \right)_{Estimate} = {\begin{Bmatrix} {{lower}\mspace{14mu} {value}} \\ {1 + {{slope} \cdot {RIWE}}} \\ {{higher}\mspace{14mu} {value}} \end{Bmatrix}\mspace{14mu} {for}\mspace{14mu} \begin{Bmatrix} {{RIWE} < {{lower}\mspace{14mu} {RWIE}\; \lim}} \\ {{{lower}\mspace{14mu} {RWIE}\; \lim} \leq {RIWE} \leq {{upper}\mspace{14mu} {RIWE}\; \lim}} \\ {{RIWE} > {{upper}\mspace{14mu} {RIWE}\; \lim}} \end{Bmatrix}}$

If RIWE falls below the lower RIWE limit, then T_(Mod)/T_(Sys) is fixed at a lower value. If RIWE exceeds the upper RIWE limit, then T_(Mod)/T_(Sys) is fixed at an upper value. Within the RIWE limits, a straight line with a definite slope is placed at the optimum value T_(Mod)/T_(Sys)=1, where in addition, RIWE=0. In this case, an error of zero occurs, i.e. the system and the model are balanced.

Using the procedure described below, in the event of deviations, the model and system parameters can be put through slight iterative changes until the model behavior and system behavior end up essentially identical. For this reason, the method preferably includes the following steps:

Association of the current characteristic number to interrelated eigenvalues of functional units by means of which the characteristic number was determined. In the above example, this would be T_(Mod)/T_(Sys).

Determination of a correction factor by evaluation or by reading from the characteristic curve. In this case, a determination is made as to which change T_(Mod)/T_(Sys) it must be subjected to in order to reduce RIWE.

Change of an eigenvalue (T_(Mod) or T_(Sys) in this case) of at least one functional unit by means of the correction factor.

The resulting change in the RIWE consequently yields a new change in the factor T_(Mod)/T_(Sys). In another step, this new determination of the characteristic number RIWE is carried out through evaluation of the characteristic curve with the new ratio of T_(Mod)/T_(Sys).

The above steps are repeated until the characteristic number essentially trends toward zero or, in accordance with a setpoint value, essentially corresponds to this setpoint value. The setpoint value is selected so that it yields a negligible error.

The sequence of such an iteration is shown by way of example in FIG. 3. The graph depicts the inverse characteristic curve of RIWE, plotted over the quotients of T_(Mod)/T_(Sys).

The curve was linearized by RIWE=0 and limited as follows:

$\left( \frac{T_{Mod}}{T_{Sys}} \right)_{Estimate} = {\begin{Bmatrix} 0.6 \\ {1 + {2 \cdot {RIWE}}} \\ 1.6 \end{Bmatrix}\mspace{14mu} {for}\mspace{14mu} \begin{Bmatrix} {{RIWE} < {- 0.2}} \\ {{- 0.2} \leq {RIWE} \leq 0.3} \\ {{RIWE} > 0.3} \end{Bmatrix}}$

The method converges within a few steps. The fewer requirements that are placed on the magnitude of the deviation, the fewer iteration steps that are required.

The evaluation is carried out as described below (all numerical values given below are approximations):

RIWE is −0.33 at the start and T_(Mod)/T_(Sys) is 0.5 at the start. Since T_(Mod)/T_(Sys) lies outside the linearized region, a change to T_(Mod)/T_(Sys), considered from the lower limit line (0,6), is carried out through modification of one or both parameters in a functional unit. This changes the quotient to T_(Mod)/T_(Sys)=0.8. In this case, based on the RIWE characteristic curve, an RWIE=−0.1 occurs, which already produces a massive error reduction in comparison to RIWE=−0.33. Since even the linearized region of the characteristic curve is relevant for the quotient T_(Mod)/T_(Sys)=0.8, T_(Mod)/T_(Sys) is modified further starting from this characteristic curve, i.e. from this point forward, the RIWE characteristic curve is no longer taken into consideration. From the characteristic curves, it is now clear that for T_(Mod)/T_(Sys), the factor of approximately 1 occurs, with RWIE in this case essentially trending toward zero. Thus in this case, it would only take a few iteration steps to eliminate the error for the most part. If necessary, further iteration steps can produce the approximation RIWE=0, depending on the desired level of precision. Depending on which of the counter functionals (3) to (5) is selected, quicker or more interference-stable results are achieved. As expectations with regard to the reduction of the RIWE rise, however, the number of iteration steps and therefore the computational complexity and time expenditure also increase.

Preferably, the third step described further above includes the following partial steps:

a) Determination of the system noise level during the adjustment, b) Establishment of a threshold that lies above the system noise level, c) Determination of the local maximum of the signal above the threshold, d) Restart of the adjustment

This makes it possible to derive a trigger signal from a reference variable change only if this trigger signal lies above the system noise level.

The beginning of the adjustment can thus be started without a separate start signal having to be generated on the control level. Thus a parameter adaptation is possible, for example, even in systems in which the guidance signal is produced manually. Let us take speed setpoint values in mobile work machines as an example. In this case, it is necessary to take into account arbitrary setpoint curves. As a trigger signal to be evaluated, it would be possible to select the second derivative {umlaut over (x)} of the reference variable x. This represents the lurch in the speed controller. It is thus possible, even in batch processing of position-controlled drive units (with limitation of lurching or acceleration), to generate a trigger signal immediately after the start of a batch.

The trigger algorithm can, for example, include the following steps:

-   -   Detection of a local maximum of {umlaut over (x)} (second         derivative of the reference variable) according to the rule         {umlaut over (x)}_(k)<{umlaut over (x)}_(k-1)         {umlaut over (x)}_(k-1)≧{umlaut over (x)}_(k-2) and noting of         the trigger value, where k−1 should represent the respective         preceding value.     -   Resetting of the adjustment if a higher trigger value is         detected during the run time.     -   Calculation of an average level of the trigger signal as an         arithmetic average of the amount of all individual values over         the adjustment period as a basis for a noise suppression.     -   Start of the evaluation if the trigger value exceeds X times the         noise level of the preceding evaluation.

As an additional stabilization measure, the result of the evaluation is discarded if the change in the system output value over the entire evaluation period has a different sign than the change in the input value. This specifically indicates an excitation of the system by interference variables.

For these purposes, the triggering means of the regulating device according to the invention is embodied in such a way that it determines the system noise level during an adjustment, automatically establishes a threshold as a function of the determined system noise level, determines the local maximum of a signal derived from a reference variable change, and implements the start of the adjustment depending on the determined maximum.

Preferably, the regulating device of an electrically operated machine, in particular a speed-controlled drive unit, functions using the method according to the invention. In a speed-controlled drive unit, the denominator functional behaves in proportion to the sum of the amount of the kinetic power of the drive unit.

$\begin{matrix} {{\int_{0}^{T_{Ref}}{{\ {{\overset{.}{y}}_{Sys} \cdot y_{Sys}}} \cdot {{sgn}\left( y_{Sys} \right)} \cdot {t}}} = {\int_{0}^{T_{Ref}}{{{a \cdot v}} \cdot {{sgn}(v)} \cdot {t}}}} \\ {= {\int_{0}^{T_{Ref}}{\frac{1}{m}{{\ {F_{a} \cdot v}} \cdot {{sgn}(v)} \cdot {t}}}}} \\ {= {\frac{1}{m}{\int_{0}^{T_{Ref}}{{P_{a}} \cdot {{sgn}(v)} \cdot {t}}}}} \end{matrix}$

In this connection, it is possible to physically illustrate how the denominator functional increases the robustness of the total error functional: the added weighted deviation of the speeds of the model y_(Mod) and the real drive unit y_(Sys) is related to the total energy expended for changing the speed of the drive unit. This value also includes power values that occur due to interference forces. In other words, if interference phenomena occur during an analysis period, then the total functional RIWE becomes correspondingly smaller; the error is therefore given less weight. It is thus possible to identify the effective drive unit inertia. The parameter “a” stands for the acceleration, “F” stands for the force, “m” stands for the mass, “P” stands for the power, and “v” stands for the speed.

This also correspondingly applies to hydraulically operated machines, particularly for pressure-controlled and valve-controlled cylinders or speed-controlled and valve-controlled cylinder drive units. For example, it is also possible to identify the hydraulic capacity based on the measurement of pressure changes or volume changes.

The invention is also advantageous for the use of pneumatically operated machines or hybrid machines that make use of a plurality of the above-mentioned principles.

Preferably, the method according to the invention and the device according to the invention relate to systems of the first order in which a fully automatic or semiautomatic implementation is conceivable.

Other aspects of the invention ensue from the following description of the drawings. The drawings show a PI-controlled system that in principle has an overshooting guidance behavior. This has been prevented through the introduction of a setpoint filter into the integral branch of the controller. By taking into account the explanations given further above, the filter used can be viewed as a model of the system with a proportional controller.

On the basis of the time responses of the filter output and the controlled variable in a certain period after excitation by means of the setpoint value change, the method according to the invention determines a correction value that can be used for parameter correction so that the model behavior and system behavior conform to each other. This method enables controller adaptation and parameter identification.

FIG. 1 gives a detailed view of part of the signal flowchart of a PI controller, which includes a proportional branch 1, a filter 2, and an integral branch 3. The drawing also shows the controlled system 4, and interference variables 6. In this case, the integral branch of the controller has a low-pass filter of the first order for the setpoint value 5 inserted into it, which, with suitable parameterization of the delay time T_(f), compensates for the zero point of the transfer function of the entire system. This measure has positive repercussions on the reaction speed both with regard to the guidance behavior and with regard to the reduction of the interference behavior. The PI control loop could also be cascaded with another control loop, e.g. a position controller for electrical servo axes.

The suitable filter parameterization for the example used here is:

$T_{f}\overset{!}{=}{T_{M} = {\frac{1}{2 \cdot D \cdot \omega_{0}} = {\frac{m}{d} = \frac{m}{K_{P}}}}}$

For the advantageous case of a parameterization of the filter in accordance with this equation, guidance behaviors of the filter (model) and system (control loop without filter) are identical. Since the filter behavior and the segment behavior do not deviate from each other, there is no control value in the I branch. The filter parameterization consequently achieves a minimization of the deviation between a model (filter) and the real system. Whether the minimization of the deviation in this case is achieved by adapting the model (filter parameterization) or the system (controller parameterization) is secondary in this context. In other words: in order to adjust system behavior and model behavior, in addition to adapting the time constant T_(f) of the filter, it is also possible to revert to using the real response time T_(M) of the speed-controlled drive unit or the changing of the controller amplification K_(P). The method according to the invention solves the parameterization in a numerical fashion. The method could, for example, be implemented by means of a control integrated into the drive unit or by means of a regulation integrated into the drive unit or external to it. The adjustment could be used, for example, in the speed control or position control of a drive unit. Possible forms of excitation for simulating a reference variable change include jumps, ramps with speed and acceleration limitations for the positioning operation, and arbitrary setpoint value signals from an overriding controller cascade. Furthermore, a deactivation window can be implemented that prevents the adoption of a newly identified parameter if it diverges from the current parameter value by less than a percentage, which is to be determined, e.g. 0.5%.

SUMMARY OF THE SYMBOLS USED

-   {dot over (x)}—speed setpoint value (reference variable) -   {dot over (x)}o—speed actual value -   F_(o)—control variable -   F_(L)—interference variable -   K_(P)—amplification factor (proportional element) -   K_(M)—torque constant -   m—mass -   T_(i)—integration element time constant -   T_(f)—filter time constant (PT1) -   T_(M)—system response time -   U—voltage -   y_(Mod)—system response of the model to a changing reference     variable -   y_(Sys)—system response of the control system to a changing     reference variable -   Δy_(Sys)—cumulative change in system response of the control system -   {dot over (y)}_(Mod)—derivatives of the model system response -   {dot over (y)}_(Sys)—derivatives of the control system response 

1. A method for adjusting a first functional unit (2) and a second functional unit (1, 3, 4, 6) of a regulating device, wherein in a first step, a characteristic number is determined, which quantifies the relative deviation of the reactions y_(Mod) and y_(Sys) of the two functional units to a reference variable change that occurs when both functional units are excited by the same reference variable change and in a second step, a correction factor is determined based on the characteristic number and this factor is used to parameterize at least one of the two functional units and reduces the relative deviation.
 2. The method as recited in claim 1, wherein in a third step, the adjustment is started by means of a signal derived from the reference variable change.
 3. The method as recited in claim 1, wherein the first functional unit (2) is a filter, preferably a low-pass filter of the first order, and the second functional unit (1, 3, 4, 6) is the control system itself.
 4. The method as recited in claim 1, wherein y_(Mod) and y_(Sys) are filtered by means of a filter device before the determination of the characteristic number.
 5. The method as recited in claim 3, wherein the characteristic number is determined in accordance with one of the following rules: $\begin{matrix} {{{characteristic}\mspace{14mu} {number}} = {\frac{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\ {{{\overset{.}{y}}_{Sys} \cdot \Delta}\; y_{Sys}}} \cdot {{sgn}\left( {\Delta \; y_{Sys}} \right)} \cdot {t}}}\mspace{14mu} {or}}} \\ \begin{matrix} {{{characteristic}\mspace{14mu} {number}} = {\frac{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{\overset{.}{y}}_{Mod} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{T_{0}}^{T_{0} + {x \cdot T_{f}}}{{{\overset{.}{y}}_{Sys} \cdot \Delta}\; {y_{Sys} \cdot {t}}}}\mspace{14mu} {or}}} & \; \end{matrix} \\ {{{{characteristic}\mspace{14mu} {number}} = \frac{\int_{\;_{0}}^{x \cdot T_{f}}{{\ {\overset{.}{y}}_{Mod}} \cdot \left( {y_{Mod} - y_{Sys}} \right) \cdot {t}}}{\int_{\;_{0}}^{x \cdot T_{f}}{{\ {{\overset{.}{y}}_{Sys} \cdot \; y_{Sys}}} \cdot {{sgn}\left( y_{Sys} \right)} \cdot {t}}}},} \end{matrix}$ where y_(Mod) and y_(Sys) represent the transient responses of the two functional units to a reference variable change, taken into account over time, the symbol {dot over (y)} is the time derivative of y, T_(f) corresponds to an eigenvalue of the first functional unit, in particular the filter time constant of a low-pass filter (2), by means of which it is possible to influence the transient response of at least one of the two functional units, x stands for a multiple of the eigenvalue, preferably for a value in the vicinity of 2*Pi, and the equation Δy_(Sys)=y_(Sys)−y_(Sys)(t=T₀) applies, where T0 corresponds to a freely selectable starting time.
 6. A method as recited in claim 1, wherein a characteristic curve for the characteristic number is determined as a function of the ratio of the eigenvalues of the first and second functional unit (1, 2, 3, 4, 6), which characteristic curve is in particular at least partially linearized in the region in which the characteristic number trends toward zero.
 7. The method as recited in claim 6, wherein the determination of the correction factor by means of the characteristic curve is carried out through successive approximation.
 8. The method as recited in claim 7, wherein it includes the following steps: a) Association of the current characteristic number to eigenvalues of functional units (1, 2, 3, 4, 6), b) Determination of the correction factor c) Change of the eigenvalue of at least one functional unit (1, 2, 3, 4, 6) by means of the correction factor, d) Redetermination of the characteristic number, e) Repetition of steps a) through d) until the characteristic number essentially trends toward zero or, in accordance with a setpoint value, essentially corresponds to this setpoint value.
 9. The method as recited in claim 2, wherein the third step includes the following partial steps: a) Determination of the system noise level during the adjustment, b) Establishment of a threshold that lies above the system noise level, c) Determination of the local maximum of the signal that lies above the threshold, d) Restart of the adjustment with a new local maximum.
 10. A regulating device with adjustment for at least two functional units (1, 2, 3, 4, 6) included in the regulating device, which functional units are configurable by means of parameterizable eigenvalues, wherein a calculation means is included, which determines, particularly in accordance with claim 5, a characteristic number that quantifies the relative deviation between the reactions y_(Mod) and y_(Sys) of the two functional units to a reference variable change when both functional units (1, 2, 3, 4, 6) are excited by the same reference variable change and which, based on the characteristic number, determines a correction factor that is used to parameterize the functional units; a correction means is included to which the correction factor is supplied and which carries out a parameterization of the functional units in such a way that this reduces the relative deviation.
 11. The regulating device as recited in claim 10, wherein a triggering means is included, which, based on a reference variable change, derives a signal that is supplied to the regulating device and triggers the start of the adjustment.
 12. The regulating device as recited in claim 10, wherein the calculation means is embodied so that it is possible to determine a nondimensional correction factor and the correction means is embodied so that it is possible to carry out an iterative change to an eigenvalue of at least one functional unit by means of the correction factor; the correction means continues to process correction values from the calculation means until the deviation essentially trends toward zero or, in accordance with a setpoint value, essentially corresponds to this setpoint value.
 13. The regulating device as recited in claim 10, wherein a means is included for deriving a characteristic curve for the characteristic number as a function of the eigenvalues of the functional units (1, 2, 3, 4, 6).
 14. The regulating device as recited in claim 13, wherein a storage means is included in which the characteristic curve is stored and to which the regulating device has access.
 15. The regulating device as recited in claim 10, wherein the triggering means is embodied in such a way that it determines the system noise level during an adjustment, automatically establishes a threshold as a function of the determined system noise level, determines the local maximum of a signal derived from a reference variable change, and implements the start of the adjustment depending on the determined maximum.
 16. The regulating device as recited in claim 10, wherein each functional unit is followed by a filter, with the respective filters having essentially identical filter properties.
 17. An electrically operated machine, in particular a drive unit, equipped with a regulating device as recited in claim
 10. 18. A hydraulically operated machine, in particular a pressure-controlled cylinder or a speed-controlled cylinder drive unit, equipped with a regulating device as recited in claim
 10. 19. A pneumatically operated machine, equipped with a regulating device as recited in claim
 10. 20. A hybrid machine as recited in claim
 17. 21. A drive control, preferably with integrated SPS, which carries out a method as recited in claim
 1. 